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Found 17 papers, 6 papers with code
BOSC: A toolbox for aerial imagery mapping
Accurate and efficient label of aerial images is essential for informed decision-making and resource allocation, whether in identifying crop types or delineating land-use patterns.
Adversarial Examples are Misaligned in Diffusion Model Manifolds
In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results.
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images.
Deep Diffusion Models for Seismic Processing
Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing.
Asset Allocation: From Markowitz to Deep Reinforcement Learning
Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon.
Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal
To the best of our knowledge, this study pioneers the unboxing of neural networks for the demultiple process, helping the user to gain insights into the inside running of the network.
Investigating Shifts in GAN Output-Distributions
In this work, we introduce a loop-training scheme for the systematic investigation of observable shifts between the distributions of real training data and GAN generated data.
Combining Transformer Generators with Convolutional Discriminators
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.
Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues
This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them.
Latent Space Conditioning on Generative Adversarial Networks
In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model.
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions
Generative convolutional deep neural networks, e. g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences.
Local Facial Attribute Transfer through Inpainting
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes.
Unmasking DeepFakes with simple Features
In this work, we present a simple way to detect such fake face images - so-called DeepFakes.
Semi Few-Shot Attribute Translation
Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications.
Object Segmentation using Pixel-wise Adversarial Loss
Recent deep learning based approaches have shown remarkable success on object segmentation tasks.
Stabilizing GANs with Soft Octave Convolutions
The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training.
